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the modularity and the high degree of addressability of self-assembled DNA nanostructures as a biosynthetic tool to probe fundamental aspects of biology. The recruited postdoc will develop two main projects: (1
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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, and how signaling pathways integrate with physical constraints to ensure an optimal balance between lipid storage and energy production. By combining quantitative biophysics, advanced imaging, and
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determining the optimal culture conditions (e.g. culture media, light intensity and temperature) and establishing the feasibility of transferring existing protocols to these species. Description of tasks
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polymer blending and colloidal lithography. Low-cost fabrication processes will be developed and optimized, and their performance will be analyzed using optical spectroscopy. Results will then be compared
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The postdoc will realize an EPR analysis on DNA-based enzymes, using 1D and 2D techniques. Two main systems will be investigated, having Copper(II) and Manganese(II) as metal co-factor. Structural and
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Two-year postdoc position (M/F) in signal processing and Monte Carlo methods applied to epidemiology
. To that aim, both Stein-based bilevel optimization, empirical Bayesian and unsupervised deep learning approaches will be considered. The recruited postdoc researcher will tackle both implementation challenges
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-level visible laser in double-clad Pr3+-doped fluoride fiber pumped by a GaN diode,” Opt. Lett. 46, 74-77 (2021). This postdoc project focuses on the development of monolithic all-fiber laser systems
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of sea turtles - Developing innovative machine learning methods to analyze the sounds associated with these behaviors - Automating the processing of audio and visual data to optimize the quantity and
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the most informative polarimetric parameters and optimal wavelengths for detecting abiotic stress. This approach will contribute to more accurate digital phenotyping, supporting sustainable crop monitoring